Article ID Journal Published Year Pages File Type
8147072 Infrared Physics & Technology 2015 13 Pages PDF
Abstract
A new signal processing algorithm combining Markov and principal component analysis (PCA) algorithm, which was named as Markov-PCA algorithm, was proposed to process the pulsed infrared thermography. First, the image sequence was reconstructed using Markov algorithm, then the original complex data dimensionality was reduced using PCA algorithm, which can remove the noise and redundancy of the infrared image sequences, and thus improve the detectability of defects. Results show that both the starting frame position and size of analysis window has an obvious effect on the processing results of Markov-PCA algorithm. And the proposed Markov-PCA algorithm improves the signal to noise ratio (SNR) of feature images more significantly than the commonly used PPT algorithm.
Related Topics
Physical Sciences and Engineering Physics and Astronomy Atomic and Molecular Physics, and Optics
Authors
, , , , ,